Communicating with Charts

Data Visualisation Workshop

Nicola Rennie
1 November 2023

Introduction

This session will include:

why you should visualise data;
some guidelines for making better charts;
examples of charts made in R;
a chance for you to make charts!

RSS Data Visualisation Guide

royal-statistical-society.github.io/datavisguide

Why visualise data?

Data visualisation has two main purposes:

Exploratory data analysis and identifying data issues
Communicating insights and results

Examples of sequential, diverging, and qualitative palettes

Exploratory data visualisation

Because summary statistics aren’t enough…

Dataset	A	B
mean_x	54.2632732	54.2658818
mean_y	47.8322528	47.8314957
sd_x	16.7651420	16.7688527
sd_y	26.9354035	26.9386081
cor_xy	-0.0644719	-0.0686092

Communicating insights with data visualisation

Grab attention

Visualisations stand out. If a reader is short on time or uncertain about whether a document is of interest, an attention-grabbing visualisation may entice them to start reading.

Improve access to information

Textual descriptions can be lengthy and hard to read, and are frequently less precise than a visual depiction showing data points and axes.

Summarise content

Visual displays allow for summarising complex textual content, aiding the reader in memorising key points.

Communicating insights with data visualisation

John Snow collected data on cholera deaths and created a visualisation where the number of deaths was represented by the height of a bar at the corresponding address in London.

This visualisation showed that the deaths clustered around Broad Street, which helped identify the cause of the cholera transmission, the Broad Street water pump.

Snow. 1854.

Tips for better charts

Choosing a chart type

What is the purpose of the chart?

Try: ft-interactive.github.io/visual-vocabulary

Why do people hate pie charts?

Source: giphy.com

Why do people hate pie charts?

The Sun front page showing the Boris Johnson pie chart

Elements of charts

Layout
Aspect ratio
Lines
Points

Colours
Axes
Symbols
Legends

Orientation
Auxiliary elements
Dimensionality

Layouts, aspect ratios, and axes

Should the y axis start at 0?

Layouts, aspect ratios, and axes

They don’t always have to start at zero…

Layouts, aspect ratios, and axes

Order categories appropriately…

Layouts, aspect ratios, and axes

Order based on magnitude unless the category order has meaning…

Source: Georgia Department of Public Health

Lines

Suggest an order
Suggest continuity

Colours

Why use colours in data visualisation?

Colours should serve a purpose, e.g. discerning groups of data
Colours can highlight or emphasise parts of your data.
Not always the most effective for, e.g. communicating differences between variables.

Colours

Different types of colour palettes…

… for different types of data.

Colours

Is this a good choice of colour?

Colours

Check for colourblind friendly plots with colorblindr::cvd_grid(g).

Colours

In base R via {RColorBrewer}: brewer.pal(4, "Dark2")
In {ggplot2}, scale_fill_brewer(), scale_fill_distiller(), scale_colour_brewer(), scale_fcolour_distiller().
Alternatives to theme_grey()!

Screenshot of colorbrewer2.org — colorbrewer2.org

Legends

Should not use up valuable space for data
May be integrated into the figure

Legends

or use {ggtext} to colour font in the subtitle…

Fonts

Arial: Does it pass the 1Il test?

Times New Roman: Does it pass the 1Il test?

Courier New: Does it pass the 1Il test?

Fonts

Font size: larger fonts are (usually) better
Font colour: ensure sufficient contrast
Font face: highlight text using bold font, avoid italics
Font family: choose a clear font with distinguishable features (pick something familiar)

There is no perfect font.

Key points

Charts should have a purpose
Actively design visualisations
Default settings aren’t always the best choices

Useful resources

Fundamentals of data visualization: clauswilke.com/dataviz
R Graphics Cookbook: r-graphics.org
#TidyTuesday: github.com/rfordatascience/tidytuesday
DataWrapper Blog: blog.datawrapper.de

TidyTuesday logo

Good charts don’t have to be boring!

Stacked diverging bar chart of lego colours — Cara Thompson (cararthompson.com)

small multiples are charts of college basketball — Cedric Scherer (cedricscherer.com)

Good charts don’t have to be boring!

Supreme court justice chart — Tanya Shapiro (tanyaviz.com)

Sloped area chart — Dan Oehm (gradientdescending.com)

Your turn!

Exercise 1

In groups, discuss the following chart. How could you improve it?

Source: medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368

05:00

Exercise 2

This data set is from a study published in 1757 in A Treatise on the Scurvy in Three Parts, by James Lind.

“Pages 149-153 are a rare gem among what can be generously described as 400+ pages of evidence-free blathering, and these 4 pages may represent the first report of a controlled clinical trial.”

study_id	treatment	dosing_regimen_for_scurvy	gum_rot_d6	skin_sores_d6	weakness_of_the_knees_d6	lassitude_d6	fit_for_duty_d6
001	cider	1 quart per day	2_moderate	2_moderate	2_moderate	2_moderate	0_no
002	cider	1 quart per day	2_moderate	1_mild	2_moderate	3_severe	0_no
003	dilute_sulfuric_acid	25 drops of elixir of vitriol, three times a day	1_mild	3_severe	3_severe	3_severe	0_no
004	dilute_sulfuric_acid	25 drops of elixir of vitriol, three times a day	2_moderate	3_severe	3_severe	3_severe	0_no
005	vinegar	two spoonfuls, three times daily	3_severe	3_severe	3_severe	3_severe	0_no
006	vinegar	two spoonfuls, three times daily	3_severe	3_severe	3_severe	3_severe	0_no
007	sea_water	half pint daily	3_severe	3_severe	3_severe	3_severe	0_no
008	sea_water	half pint daily	3_severe	3_severe	3_severe	3_severe	0_no
009	citrus	two lemons and an orange daily	1_mild	1_mild	0_none	1_mild	0_no
010	citrus	two lemons and an orange daily	0_none	0_none	0_none	0_none	1_yes
011	purgative_mixture	a nutmeg-sized paste of garlic, mustard seed, horseradish, balsam of Peru, and gum myrrh three times a day	3_severe	3_severe	3_severe	3_severe	0_no
012	purgative_mixture	a nutmeg-sized paste of garlic, mustard seed, horseradish, balsam of Peru, and gum myrrh three times a day	3_severe	3_severe	3_severe	3_severe	0_no

Exercise 2

Edit the code to improve this chart:

library(ggplot2)
library(medicaldata)
data("scurvy")

ggplot(data = scurvy) +
  geom_col(mapping = aes(
    x = study_id,
    y = gum_rot_d6,
    fill = treatment
  )) +
  theme(legend.position = "bottom")

15:00

Exercise 3

Bring your own chart! (Or make a new one!)

Data sources:

library(medicaldata)
#TidyTuesday: github.com/rfordatascience/tidytuesday

Source: giphy.com

Workshop resources

GitHub: github.com/nrennie/chicas-data-viz-workshop

Slides: nrennie.github.io/chicas-data-viz-workshop

Questions?

n.rennie@lancaster.ac.uk